User Group Analytics: Discovery, Exploration and Visualization

Half-day tutorial

Time: Friday, 26 October 2018, 08:15AM-09:45AM & 10:15AM-11:45AM
Room: Varsavia

Behrooz Omidvar-Tehrani

University of Grenoble Alpes, France

Behrooz Omidvar-Tehrani is a postdoctoral researcher in the University of Grenoble Alpes, France. Previously, he was a post-doctoral researcher at the Ohio State University, USA. His research is in the area of data management, focusing on interactive analysis of user data. Behrooz received his PhD in Computer Science from University of Grenoble Alpes, France. He has published in several international conferences and journals including CIKM, ICDE, VLDB, EDBT and KAIS. Also, he has been a reviewer for several conferences and journals including Information Systems, TKDE, DAMI, CIKM, ICDE, and AAAI.

Sihem Amer-Yahia

Laboratoire d’Informatique de Grenoble, CNRS, France

Sihem Amer-Yahia is a Research Director at LIG in Grenoble where she leads the SLIDE team. Her interests are at the intersection of large-scale data management and user data exploration. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at AT&T Labs. Sihem served on the SIGMOD Executive Board, the VLDB Endowment, and the EDBT Board. She is the Editor-in-Chief of the VLDB Journal and has been on the editorial boards of TODS and the Information Systems Journal. She is chairing VLDB 2018 and WWW Tutorials 2018 and will be chairing ICDE Tutorials 2019 and WWW Workshops 2019. Sihem received her Ph.D. in CS from Paris-Orsay and INRIA in 1999, and her Diplôme d’Ingénieur from INI, Algeria.


User data is becoming increasingly available in various domains from the social Web to patient health records. User data is characterized by a combination of demographics (e.g., age, gender, occupation) and user actions (e.g., rating a movie, following a diet). Domain experts rely on user data to conduct large-scale population studies. Information consumers rely on the social Web for routine tasks such as finding a book club. User data analytics is usually based on identifying group-level behaviors such as “countryside teachers who watch Woody Allen movies.” User Group Analytics (UGA) addresses peculiarities of user data such as noise and sparsity. This tutorial reviews research on UGA and discusses different approaches and open challenges for group discovery, exploration, and visualization.

Detailed Outline

Introduction to User Group Analytics (UGA): Motivations, Definitions, and Challenges
What is User Group Analytics (UGA)?
What is user data and user groups?
Real use cases in various application domains such as advertisement, program committee formation and quantified-self
Overview of UGA challenges
UGA Components: Discovery, Exploration, and Visualization
User group discovery: defining the group discovery process, and categorizing related work into attribute-based and action-based discovery
User group exploration: defining the group exploration process, and categorizing related work into by-query, by-facet, by-example, and by-analytics exploration
User group visualization: defining the building blocks of group visualization and mapping functions, and categorizing related work into graph-based and map-based visualization
UGA Evaluation: Challenges and Measures
Challenges and measures for evaluating each UGA component
Combining UGA Discovery and UGA Visualization
Challenges of combining discovery and visualization
Discussion of approaches for combining discovery and visualization in the following categories: distribution-based, facet-based, relation-based, and time-based
Discussion of visualization approaches for representing discovered groups, such as self-organizing maps, belt charts, and regression heatmaps
Combining UGA Discovery and UGA Exploration
Challenges of combining discovery and exploration
Discussion of approaches for combining discovery and exploration
Discussion of different approaches of explicit and implicit feedback capture to enable exploratory discovery
Combining UGA Exploration and UGA Visualization
Challenges of combining exploration and visualization
Discussion of approaches for combining exploration and visualization (aka, Visual Analytics)
All-In-One UGA: Challenges and Research Directions
Benefits of building an all-in-one system where all UGA components are integrated
Challenges of integrating UGA components
Discussion of recently established literature of all-in-one UGA
Discussion of future directions towards a full-fledged integration

Link to External Resources

Tutorial resources